In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company.

In January, my coworker received a peculiar email. The message, which she forwarded to me, was from a handful of corporate Walmart employees calling themselves the "Concerned Home Office Associates." While it's not unusual for journalists to receive anonymous tips, they don't usually come with their own slickly produced videos. The employees said they were "past their breaking point," with Everseen, a small artificial intelligence firm based in Cork, Ireland, whose technology Walmart began using in 2017. Walmart uses Everseen in thousands of stores to prevent shoplifting at registers and self-checkout kiosks.

CVS Health will try delivering prescriptions with self-driving vehicles in a test that begins next month. The drugstore chain said Thursday that it will partner with the Silicon Valley robotics company Nuro to deliver medicines and other products to customers near a Houston-area store. A CVS spokesman said the prescriptions will routinely be delivered within an hour of being ordered. Customers will have to confirm their identity in order to unlock their delivery after the vehicle arrives. Nuro has previously started partnerships to test the delivery of pizzas for Domino's or groceries for Kroger, also in the Houston area.

CVS Health will try delivering prescriptions with self-driving vehicles in a test that begins next month. The drugstore chain said Thursday that it will partner with the Silicon Valley robotics company Nuro to deliver medicines and other products to customers near a Houston-area store. A CVS spokesman said the prescriptions will routinely be delivered within an hour of being ordered. Customers will have to confirm their identity in order to unlock their delivery after the vehicle arrives. Nuro has previously started partnerships to test the delivery of pizzas for Domino's or groceries for Kroger, also in the Houston area.

As more online brands look for ways to move beyond third-party cookies as a way of gaining more direct insights about their users and customers, a startup that has developed a platform to help them has raised a big round of funding. Bluecore, a marketing technology firm that uses data gained from direct marketing like email, social media, site activity and combines that with machine learning to make better predictions about who might want to buy what among its customers, is today announcing that it has raised $50 million. The funding will be used to build the next generation of the Bluecore platform, expected later this year, which will tap into aggregated engagement data (but not actual browsing individuals) from "hundreds" of brands, which customers can combine with their own first-party data -- based on consent-based, first-party customer IDs -- to develop better targeting insights. "There are a lot of systems that focus on customer data and transactional data but no system that focuses on the product and product catalogue, which we think is the key asset," said Fayez Mohamood, the co-founder and CEO, in an interview. He says that the company manages over 200 million products and SKUs, second only to Amazon's and bigger than Walmart's, that companies can matches with consumer identities (from email and other direct channels).

At re:Invent 2019, AWS shared the fastest training times on the cloud for two popular machine learning (ML) models: BERT (natural language processing) and Mask-RCNN (object detection). To train BERT in 1 hour, we efficiently scaled out to 2,048 NVIDIA V100 GPUs by improving the underlying infrastructure, network, and ML framework. Today, we're open-sourcing the optimized training code for ALBERT (A Lite BERT), a powerful BERT-based language model that achieves state-of-the-art performance on industry benchmarks while training 1.7 times faster and cheaper. This post demonstrates how to train a faster, smaller, higher-quality model called ALBERT on Amazon SageMaker, a fully managed service that makes it easy to build, train, tune, and deploy ML models. Although this isn't a new model, it's the first efficient distributed GPU implementation for TensorFlow 2. You can use AWS training scripts to train ALBERT in Amazon SageMaker on p3dn and g4dn instances for both single-node and distributed training.

The mobile app development industry is taking the world by storm, with the new technologies trending across the market. Geolocation is one such trend that is predicted to reach a revenue of $40 billion by 2024 for the location-based service market, according to MarketsandMarkets report. Geolocation applications open up new opportunities for entrepreneurs to explore. They are also a gateway for the service industry to improve customer experience and interact with their user base. The rising number of location-based apps, like on-demand service apps, is proof that geolocation is quintessential to survival in the mobile app market.

Amazon.com Inc. is in talks to buy driverless vehicle startup Zoox Inc., according to a person familiar with the matter, a deal that would accelerate the e-commerce giant's automation efforts. "Zoox has been receiving interest in a strategic transaction from multiple parties and has been working with Qatalyst Partners to evaluate such interest," the startup said. It declined to comment on Amazon's interest. A spokeswoman for Amazon declined to comment. Zoox had outsized ambition and financial backing.

Working together, they move the bins around nonstop, accessing specific items and delivering them to the people on the outside. On a busy day, these robots churn through 20,000 online orders, 80% of which are placed via smartphones. A growing number of retailers are turning to this kind of automation to out-compete their rivals. Robots keep costs down and make order fulfillment quicker and more accurate. Now, given a series of lockdowns that could go on for months or even years, this kind of small-scale automation could be key if retailers are to survive.

As we've seen unfold in recent years, artificial intelligence (AI), machine learning (ML) and data analytics are rapidly changing the speed at which the retail industry operates. As these technologies become increasingly popular among leading retail companies, it's clear that early adopters of AI have seen a sizable financial advantage compared to retailers that haven't yet adopted the technology. Non-adopters will need to erode their margin to stay competitive on price, while adopters with sizable financial gain will be able to weather volatility on price inputs. AI is being used as a differentiating factor between smaller retailers as a way to get ahead and capture market share. The gap between adopters and non-adopters will continue to grow, meaning AI is no longer just a way to get ahead of competitors -- it's become a pivotal part of staying relevant in the industry and maintaining innovation.